Cooperative Agreement for CESU-affiliated Partner with Desert Southwest Cooperative Ecosystem Studies Unit | G21AS00557

Frequently Asked Questions (FAQs)

What type of funding opportunity is this?

This opportunity is a discretionary Cooperative Agreement issued by the U.S. Geological Survey (USGS) under the Cooperative Ecosystem Studies Unit (CESU) Program, specifically through the Desert Southwest CESU.

What does it mean that this is a Cooperative Agreement?

A cooperative agreement is a federal assistance award that is expected to involve substantial involvement from the federal partner (here, USGS). In practice, that means the funded work is intended to be carried out collaboratively with USGS rather than as a fully independent effort by the awardee.

Which CESU is sponsoring this opportunity?

The opportunity is offered through the Desert Southwest Cooperative Ecosystem Studies Unit (CESU).

What is the CESU Program and why does it matter for this grant?

CESUs are formal partnerships designed to streamline collaboration among federal agencies and eligible partner institutions. They support research, technical assistance, and education tied to public resource management needs. For this opportunity, CESU membership is central because eligibility is limited to official Desert Southwest CESU partners.

What is the main purpose of the project?

The core purpose is to strengthen the USGS Water Mission Area (WMA) modeling toolkit by (1) advancing large-scale, physically based hydrologic simulators and (2) improving the ability to build machine learning emulators that reproduce the behavior of those physics-based models.

What kinds of work are supported under this opportunity?

The project is framed as research and development in science and technology. The description points to work that improves physically based hydrologic simulation at large scales and develops credible machine learning emulators that approximate those simulations at much lower computational cost.

What are "physically based hydrologic simulators" in this context?

They are computational models that simulate water movement and storage using governing physical processes. Examples of processes mentioned include precipitation-runoff generation, infiltration, soil moisture dynamics, groundwater-surface water exchange, evapotranspiration, and channel routing.

Why is USGS focusing on large-scale or national/basin-scale hydrologic modeling?

The opportunity emphasizes that physically based simulators can be scientifically defensible and detailed, but they often require extensive data and computing resources, especially for national-scale domains or major river basins. The goal is to make these models more capable, scalable, and useful for large-domain applications.

What is a machine learning emulator?

An emulator is a machine learning model trained to approximate the outputs of a complex physics-based simulator. The intent is to produce similar results much faster and at lower computational cost than repeatedly running the full simulator.

Why are machine learning emulators important for this opportunity?

Emulators can enable workflows that require many model evaluations. The announcement specifically highlights uses such as uncertainty quantification, sensitivity analysis, scenario planning, calibration, data assimilation, and ensemble forecasting.

Is this opportunity about replacing physics-based models with machine learning?

Based on the description, the emphasis is on a combined strategy: continuing to advance physics-based simulation while developing machine learning emulators that extend the practical reach of those simulators by reducing runtime and computational burden.

How are the project results expected to be used?

The work is intended to contribute directly to USGS national and river basin modeling efforts. This signals that the outputs are expected to support operationally relevant modeling priorities and be suitable for integration into broader USGS workflows or modeling frameworks.

What kinds of improvements could be in scope for the physics-based simulators?

The description indicates potential contributions could include improving model formulations, enhancing numerical methods, enabling better coupling among hydrologic components, increasing computational performance on modern hardware, improving reproducibility and software engineering practices, or producing approaches that better support large-domain applications.

What kinds of deliverables could be in scope for emulator development?

The opportunity emphasizes building validated emulator approaches that USGS can trust to reproduce physics-based model behavior while providing high performance (faster runtimes). The overarching goal is credible emulation that supports repeated-run use cases such as calibration and scenario analysis.

Who is eligible to apply?

Eligibility is limited to organizations that are official participating partners of the Desert Southwest CESU Program. Organizations that are not part of that CESU partnership are not eligible under this announcement, even if they have relevant expertise.

The eligible applicant category is listed as "Others." What does that mean here?

Within CESU practice, "Others" typically refers to the specific types of partner institutions recognized by the CESU network (for example, universities, nonprofits, and other approved entities), as long as they are formal members of the relevant CESU. The key requirement in the listing is official Desert Southwest CESU partnership status.

What federal agency is issuing the opportunity?

The opportunity is issued by the U.S. Geological Survey (USGS), which is listed under the Geological Survey.

What is the opportunity number?

The opportunity number is G21AS00557.

What is the CFDA number for this opportunity?

The CFDA number listed is 15.808.

What is the maximum award amount?

The award ceiling is listed as $200,000, which indicates the maximum anticipated funding level for a single award under this announcement.

How many awards will be made?

The expected number of awards is not clearly specified in the provided information. The only explicit funding detail provided is the $200,000 ceiling.

When was the opportunity posted and when did it close?

The creation date is June 9, 2021, and the original closing date is July 9, 2021.

What is the general topic area or category of this grant?

The opportunity is categorized under science and technology and other research and development, consistent with its focus on hydrologic simulation and machine learning emulation.

Which USGS program area is specifically referenced?

The opportunity explicitly references the USGS Water Mission Area (WMA) modeling toolkit and indicates that the funded work should strengthen that toolkit.

Is the work intended to be exploratory research or tied to operational priorities?

The description suggests it is not purely exploratory. The work is intended to contribute directly to USGS national and river basin modeling efforts, implying outputs should be aligned with broader USGS modeling priorities and usable in USGS workflows.